An Improved Variable Step-Size Convex Combination of LMS Adaptive Filtering Algorithm and Its Analysis

HONG Dan-feng; MIAO Jun; SU Jian; WU Xin; PAN Zhen-kuan

doi:10.3969/j.issn.0372-2112.2014.11.015

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An Improved Variable Step-Size Convex Combination of LMS Adaptive Filtering Algorithm and Its Analysis

更新时间：2025-07-16

- An Improved Variable Step-Size Convex Combination of LMS Adaptive Filtering Algorithm and Its Analysis
- Acta Electronica Sinica Vol. 42, Issue 11, Pages: 2225-2230(2014)
- 作者机构：
  
  1. 青岛大学信息工程学院,山东,青岛,266071
  2. 海军航空工程学院研究生管理大队,山东,烟台,264001
  3. 电子科技大学通信与信息工程学院射频集成电路研究中心,四川,成都,611731
  4. 青岛大学信息工程学院,山东,青岛,266071
  5. 海军航空工程学院研究生管理大队,山东,烟台,264001
  6. 电子科技大学通信与信息工程学院射频集成电路研究中心,四川,成都,611731
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61170106)
- DOI：10.3969/j.issn.0372-2112.2014.11.015
  CLC： TN911.4
- Published：2014
- 稿件说明：
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HONG Dan-feng, MIAO Jun, SU Jian, et al. An Improved Variable Step-Size Convex Combination of LMS Adaptive Filtering Algorithm and Its Analysis[J]. Acta Electronica Sinica, 2014, 42(11): 2225-2230.
DOI：

HONG Dan-feng, MIAO Jun, SU Jian, et al. An Improved Variable Step-Size Convex Combination of LMS Adaptive Filtering Algorithm and Its Analysis[J]. Acta Electronica Sinica, 2014, 42(11): 2225-2230. DOI： 10.3969/j.issn.0372-2112.2014.11.015.

摘要

为了避免单个滤波器在收敛速度与稳态误差上相互制约

从而导致系统性能降低的问题

本文采用凸组合最小均方算法(Combined Least Mean Square

CLMS)

将快速滤波器和慢速滤波器并联使用

同时为进一步改善CLMS算法的性能

对已有的变步长凸组合最小均方算法(Variable Step-size Convex Combination of LMS

VSCLMS)做出改进

提出了一种新的VSCLMS算法.在该算法中

对快速滤波器选用以最小均方权值偏差(Minimization of Mean Square Weight Error

MMSWE)为准则的按步分析的变步长滤波器;对慢速滤波器采用以稳态最小均方误差(Least Mean Square

LMS)为准则的固定步长滤波器.通过理论分析与仿真实验表明

该算法能够在噪声、时变以及非平稳的环境下保持较好的随动性能

且在各个阶段均保持良好的收敛性

与传统的CLMS、VSCLMS算法相比

不仅具有更快的收敛速度

而且拥有稳定的均方性能和较优的跟踪性能

为自适应滤波算法的研究提供了一条可行途径.

Abstract

In order to avoid the conflict between the convergence speed and stable state error for a single LMS filter

and degrade the performance of the recognition system

we used combined least mean square (CLMS) algorithm which is the parallel of fast LMS filter and slow LMS filter.Meanwhile

to further improve the performance of the CLMS algorithm

a new variable step-size convex combination of LMS (VSCLMS) algorithm was proposed by improving original VSCLMS.In the proposed algorithm

we considered the variable step-size filter on the basis of minimum mean square weight error (MMSWE) as the fast LMS filter

and a new constant step-size filter based on steady-state LMS is used as the slow filter.By analyzing theory and experimental results

the proposed algorithm

which compared with the original VSCLMS algorithm and CLMS algorithm

not only has a superior capability of tracking under the environment of noise

time-varying and unstable condition

but also can maintain a better convergence.

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